Eugenia Cornejo M., Koczy L.T., Medina J., Ramírez-Poussa E. (eds.) Computational Intelligence and Mathematics for Tackling Complex Problems 5

Springer, 2024. — 151 p. — (Studies in Computational Intelligence 1127). — ISBN 978-3-031-46978-7.This book is focused on connecting two interesting research areas, mathematics and Computational Intelligence, by means of appealing contributions devoted to give solutions to different challenges of the current technological age.
It continues the collection of articles dealing with the important and efficient combination of these both areas, with a stress of fuzzy systems and fuzzy logic. It also includes relevant papers on the development and application of mathematics, Artificial Intelligence (AI), and automatic reasoning tools to Digital Forensics.
The chapter “Efficiency of Fuzzy Rough Set Decision Algorithms” studies decision algorithms given in Rough Set Theory. Decision algorithms are used in this theory to know if a set of decision rules is suitable to describe a decision table. One of the most important notions associated with decision algorithms is the notion of efficiency. In this work, the authors present a generalized notion of efficiency, considering the fuzzy framework.The chapter “Use of Fuzzy Time Series to Generate Linguistic Descriptions of Noise Pollution” introduces a proposal to represent sets of time series by means of fuzzy time series composed of linguistic labels. This representation increases the interpretability of the fuzzy time series by using linguistic labels, which can be defined by an expert in the domain. The authors use the Monte Carlo method in order to match each fuzzy set in the initial fuzzy time series with the considered linguistic labels.
The chapter “Preference in Multi-adjoint Logic Programming Based on Ordered Adjoint Pairs” analyzes how the choice of different ordered adjoint pairs can affect the computation of the minimal model of a fuzzy multi-adjoint logic program, with the primary goal of studying preferences in decision-making frameworks.
The chapter “Effective Contract-Net-Based Intrusion Detection Using Intelligent Agent-Based Systems” addresses the problem of intrusion detection. This work proposes an intelligent agent security approach for adopting Intrusion Detection Systems in a distributed computational network-based environment, which provides advantages such as scalability or no single point of failure.Additional Notes on Heterogeneous Concept-Forming Operators
On Measuring the Solvability of a Fuzzy Relation Equation
Efficiency of Fuzzy Rough Set Decision Algorithms
Use of Fuzzy Time Series to Generate Linguistic Descriptions of Noise Pollution
A Novel Fingerprint Identification Fuzzy System Using a Center-Distance Weighted Local Binary Pattern
Global Sensitivity Analysis and Low Magnitude Pruning for Convolutional Neural Networks Reduction in Image Net Based on Transfer Learning State of the Art Models
Generalized Linearity of Aggregation and Related Functions
Closure Operators on AMV-Valued Fuzzy Sets
An Application of a Fuzzy Multi-criteria Decision Making Process for Explainable Machine Learning in the Actuarial Context
A Software Tool for Solving the Traveling Salesman Problem and Related Non-fuzzy and Fuzzy Optimization Problems
Characterizing Clarify and Reduce Method by Means of Left-Sided Formal Concept Analysis
ITAMACDSS: A Fuzzy Clinical Diagnostic Support System for Coeliac Disease
Preference in Multi-adjoint Logic Programming Based on Ordered Adjoint Pairs
Lattice Valued Aggregation Operators Applicable in Digital Forensics
Power Consumption-Based Identification of Used Encryption Solution in IoT Environments
Maritime Cyber-Attacks Detection Based on a Convolutional Neural Network
Epistemic Logic and Theory of Mind for Modelling Group Dynamics of Criminal Organizations
Effective Contract-Net-Based Intrusion Detection Using Intelligent Agent-Based Systems
Analysing the Influence of the Pandemic on Crime Patterns in North Macedonia